8 ways quantum computing could change the world

Here’s how quantum computing is set to reshape industries from finance to pharma with powerful real-world applications

Quantum computing has long lived in the realm of theory, but now it’s entering a new era of practical experimentation and commercial ambition. Tech giants, governments, and startups are investing significant resources in unlocking the commercial potential of quantum computing, and for good reason. 

These groups have their sights set on more than just the science — they want to explore opportunities for practical business use. Here’s how this once esoteric technology could redefine key sectors of the global economy, from pharmaceuticals to finance.

The research and development of new drugs is a slow and expensive process, often taking over a decade and billions of dollars. Quantum computing could change this by simulating how molecules behave at the quantum level, something traditional computers can’t do accurately. This means pharma companies could test thousands of compounds virtually, identifying promising candidates faster and at a lower cost. 

Companies like Roche and Pfizer are already exploring quantum tools to speed up research and development pipelines and rapidly respond to emerging health threats.

The financial world runs on probabilities, predicting risk, optimizing portfolios, and detecting fraud. Quantum computers are especially good at solving complex optimization problems, such as dynamically adjusting investment portfolios, making them powerful tools for asset management and risk modeling — assessing potential financial losses based on various factors. 

Major banks are investing in quantum research to explore everything from real-time trading strategies to fraud detection systems that spot subtle anomalies faster than current AI models.

AI and quantum computing are colliding in big and meaningful ways. Classical computers struggle to train large machine learning models, especially those involving vast data sets and multidimensional variables — those with multiple attributes, dimensions, or features. Quantum machine learning (QML) could significantly accelerate these tasks by processing information in more complex ways.

While QML is still in its early stages of development, companies are experimenting with hybrid systems. These classical and quantum systems work in harmony, enhancing pattern recognition, reducing training time, and enabling the creation of smarter AI tools with less data.

Supply chains can be challenging to manage due to delays, fluctuations in demand, and inefficiencies in routes, which can result in significant costs for businesses. Quantum computing’s ability to process massive datasets simultaneously makes it well-suited for logistical optimization, whether it’s planning delivery routes or managing inventory in real time.

Retailers and logistics firms already are testing scenarios where quantum systems might cut fuel use, shorten delivery windows, and improve demand forecasting — the process of predicting how much of a product or service customers will want in the future — across global supply networks.

At the atomic level, creating better materials involves understanding how electrons interact — specifically, how they move and influence one another within atoms and molecules. This task is so complex that it exceeds the limits of current supercomputers, which are highly powerful computers designed to perform complex calculations and process large amounts of data at high speeds. 

Quantum simulations could handle this complexity and simulate these behaviors more effectively, helping researchers design lighter aircraft alloys, stronger industrial materials, or more efficient battery chemistries.

For instance, NASA and leading energy companies are investing in quantum computing systems — hardware and software designed to run quantum algorithms — to discover new materials that could reduce emissions or enhance performance in extreme environments.

Ironically, quantum computing poses one of the biggest threats to data security, yet it is also one of its best defenses. Powerful quantum computers could eventually break widely used encryption methods like Rivest-Shamir-Adleman (RSA), which cover everything from email security to cryptocurrency protection. 

To prepare for this, experts are developing quantum-safe encryption methods — algorithms designed to stay secure even if quantum attacks are possible. Tech firms and cybersecurity providers are working to test and implement these new standards before large-scale quantum systems are built.

Modeling complex climate systems or predicting future energy demands involves a huge number of factors. Quantum systems can simulate these interactions far more efficiently than classical models, enabling scientists and policymakers to understand and potentially address some of the most pressing environmental challenges. 

Quantum computing could optimize power grids, model carbon capture strategies, or identify the most effective renewable energy deployments for a given region.

Quantum radar, secure satellite communications, and complex battlefield simulations are examples of how defense agencies see quantum computing as a way to gain a technological advantage. 

Quantum radar, for example, could detect stealth aircraft by using entangled particle pairs — pairs of particles connected in a way that lets them reveal hidden objects traditional radar might miss. Quantum-enabled encryption could also make military communications harder to intercept or break.

Agencies ranging from the U.S. Department of Defense to NATO are funding research into these capabilities, recognizing the potential strategic benefits quantum computing could provide.